Tracing Model Research Articles

Geographical Origin Traceability of Navel Oranges Based on Near-Infrared Spectroscopy Combined with Deep Learning

The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall navel oranges were selected from five major production regions in China, and the diffuse reflectance near-infrared spectrum in 4000–10,000 cm−1 were non-invasively collected. We examined seven preprocessing techniques for the spectra, including Savitzky–Golay (SG) smoothing, first derivative (FD), multiplicative scattering correction (MSC), combinations of SG with MSC (SG+MSC), SG with FD (SG+FD), MSC with FD (MSC+FD), and three combined (SG+MSC+FD). A one-dimensional convolutional neural network (1DCNN) deep learning model for geographical origin tracing of navel orange was established, and five machine learning algorithms, i.e., partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN), were compared with 1DCNN. The results show that the 1DCNN model based on the SG+FD preprocessing method achieved the optimal performance for the testing set, with prediction accuracy, precision, recall, and F1-score of 97.92%, 98%, 97.95%, and 97.90%, respectively. Therefore, NIRS combined with deep learning has a significant research and application value in the rapid, nondestructive, and accurate geographical origin traceability of agricultural products.

Product tracing or component tracing? Blockchain adoption in a two-echelon supply chain management

Consumer awareness of product authenticity and carbon footprint tracing are among the most remarkable reasons for adopting blockchain in the supply chain in today’s world. However, the research literature has not yet examined specific ways to adopt blockchain in the supply chain. This study aims to develop a decision support tool to deal with the adoption of blockchain technology to design a two-echelon supply chain. In this regard, four specific cases for integrating supply chain and blockchain are developed based on types of tracing and block generation authority. In product tracing, green products are investigated throughout the supply chain, while in component tracing, green products are examined between the components of the supply chain. As it is necessary to record and verify the supply chain information by authorities in the blockchain network, in this work, such authorities are taken into account for both links and members of the supply chain. As far as we know, this is the first attempt to classify the various methods of adopting blockchain in Green Supply Chain Management (GSCM) and propose mathematical optimization models related to them. In this line, four Mixed-Integer Linear Programming (MILP) models with the aim of minimizing the costs related to the physical supply chain and blockchain deployment are developed for the integration of the supply chain with blockchain technology. They are treated by the Branch and Efficiency (B&E) algorithm and Simultaneous Data Envelopment Analysis (SDEA) model considering common (cost and service) and innovative (blockchain) criteria. The results showed that link-based and component tracing models are cost-effective. In addition, the cost objective function of green product tracing is more sensitive to the number of blocks than that of component tracing. Eventually, the study provides great opportunities for decision-makers and managers to understand how to adopt blockchain in terms of supply chain network characteristics, cost, transparency, and service.

Deep Knowledge Tracing Integrating Temporal Causal Inference and PINN

Knowledge tracing predicts students’ future performance based on their historical performance data, which is significant for students’ learning resource recommendation, learning path prediction, and other aspects. Students’ knowledge mastery, learning ability, and question difficulty all influence the performance metrics of knowledge tracing. This paper proposes a deep knowledge tracing model that integrates temporal causal inference and the PINN (Physics-Informed Neural Network) model. The model first uses the temporal causality model to explores the causal relationships between students’ knowledge points, which is then combined with the deep learning-based knowledge tracing model for prediction. Next, it treats the logical model as a ’physical model’, adds a loss term, considers the confounding factors caused by students’ answer preferences, and adjusts students’ learning ability through backdoors to obtain more accurate predictions. In the public education datasets ASSISTment2012 and ASSISTchall, the predictive performance of the TLPKT-PINN model is superior to some classical models and LPKT. From the experimental results, we can conclude that considering the degree of mastery of causal knowledge points and adjusting the loss term can improve the accuracy of predicting student grades.

Outcomes of Zero Power Intraocular Lenses: Insights into the Keratometric Index and Axial Length.

We analyzed intraocular lens (IOL) power calculation in a series of patients with a zero diopter power implant. The idea was to bypass the Estimated Lens Plane (ELP), an important variable in normal IOL calculation. Large multi-center group practice. Retrospective, observational study. A consecutive series of patients undergoing cataract surgery after optical biometry and implantation of a zero (0.0 D) power IOL were studied. Complete biometry was performed with an optical low coherence optical biometer. The predicted refraction was calculated with and without recalibrating the axial length (AL) using a Sum-Of-Segments (SOS) approach with the Hoffer Q, Holladay 1, SRK/T, Haigis, Barrett Universal II, and Olsen formulas. A ray tracing model to back-calculate the corneal power from the observed postoperative refraction and AL was also included. In 52 eyes of 52 patients, mean prediction errors for the Hoffer Q, Holladay 1, SRK/T, Haigis, Barrett Universal II, and the Olsen formulas were +1.48, +1.15, +0.91, +0.65, +0.15 and +0.11 D respectively using standard AL and after SOS correction of the AL the mean errors changed to +1.08, +0.75, +0.52, +0.26, -0.25, and -0.27 D respectively. The mean back-calculated corneal power was 1.25 D lower than the mean keratometer reading using the standard keratometric index of 1.3375 and corresponded to an effective index of 1.328. The hyperopic error commonly seen in highly myopic eyes with classical thin-lens formulas is only partly corrected by SOS recalibration of the AL. We suggest the source of the residual error is an incorrect keratometric index.

Enhancer-driven Shh signaling promotes glia-to-mesenchyme transition during bone repair

Plp1-lineage Schwann cells (SCs) of peripheral nerve play a critical role in vascular remodeling and osteogenic differentiation during the early stage of bone healing, and the abnormal plasticity of SCs would jeopardize the bone regeneration. However, how Plp1-lineage cells respond to injury and initiate the vascularized osteogenesis remains incompletely understood. Here, by employing single-cell transcriptional profiling combined with lineage-specific tracing models, we uncover that Plp1-lineage cells undergoing injury-induced glia-to-MSCs transition contributed to osteogenesis and revascularization in the initial stage of bone injury. Importantly, our data demonstrated that the Sonic hedgehog (Shh) signaling was responsible for the transition process initiation, which was strongly activated by c-Jun/SIRT6/BAF170 complex-driven Shh enhancers. Collectively, these findings depict an injury-specific niche signal-mediated Plp1-lineage cells transition towards Gli1+ MSCs and may be instructive for approaches to promote bone regeneration during aging or other bone diseases.

BPSKT: Knowledge Tracing with Bidirectional Encoder Representation Model Pre-Training and Sparse Attention

Knowledge tracing (KT) is a core task in an intelligent education system. It is designed to track the changes in students’ knowledge states during practice, and further predict the accuracy of their answers in the next round of exercises. Current knowledge tracing models generally focus on solving short-sequence problems, and still have significant limitations when handling long-sequence problems. Additionally, there is considerable room for improvement in the experimental performance of existing models on sparse datasets. This paper proposes a BPSKT knowledge tracing model that combines BERT pre-training and sparse attention. It extracts long-sequence node information features through two layers of GCN, and then fine-tunes the pre-trained data using sparse attention BERT to adapt it for downstream tasks. Finally, through a series of validation experiments, the logical consistency and structural effectiveness of BPSKT are gradually verified.

Targeting aldolase A in hepatocellular carcinoma leads to imbalanced glycolysis and energy stress due to uncontrolled FBP accumulation.

Increased glycolytic flux is a hallmark of cancer; however, an increasing body of evidence indicates that glycolytic ATP production may be dispensable in cancer, as metabolic plasticity allows cancer cells to readily adapt to disruption of glycolysis by increasing ATP production via oxidative phosphorylation. Using functional genomic screening, we show here that liver cancer cells show a unique sensitivity toward aldolase A (ALDOA) depletion. Targeting glycolysis by disrupting the catalytic activity of ALDOA led to severe energy stress and cell cycle arrest in murine and human hepatocellular carcinoma cell lines. With a combination of metabolic flux analysis, metabolomics, stable-isotope tracing and mathematical modelling, we demonstrate that inhibiting ALDOA induced a state of imbalanced glycolysis in which the investment phase outpaced the payoff phase. Targeting ALDOA effectively converted glycolysis from an energy producing into an energy-consuming process. Moreover, we found that depletion of ALDOA extended survival and reduced cancer cell proliferation in an animal model of hepatocellular carcinoma. Thus, our findings indicate that induction of imbalanced glycolysis by targeting ALDOA presents a unique opportunity to overcome the inherent metabolic plasticity of cancer cells.

LGS-KT: Integrating logical and grammatical skills for effective programming knowledge tracing.

Knowledge tracing (KT) estimates students' mastery of knowledge concepts or skills by analyzing their historical interactions. Although general KT methods have effectively assessed students' knowledge states, specific measurements of students' programming skills remain insufficient. Existing studies mainly rely on exercise outcomes and do not fully utilize behavioral data during the programming process. Therefore, we integrate a Logical and Grammar Skills Knowledge Tracing (LGS-KT) model to enhance programming education. This model integrates static analysis and dynamic monitoring (such as CPU and memory consumption) to evaluate code elements, providing a thorough assessment of code quality. By analyzing students' multiple iterations on the same programming problem, we constructed a reweighted logical skill evolution graph to assess the development of students' logical skills. Additionally, to enhance the interactions among representations with similar grammatical skills, we developed a grammatical skills interaction graph based on the similarity of knowledge concepts. This approach significantly improves the accuracy of inferring students' programming grammatical skill states. The LGS-KT model has demonstrated superior performance in predicting student outcomes. Our research highlights the potential application of a KT model that integrates logical and grammatical skills in programming exercises. To support reproducible research, we have published the data and code at https://github.com/xinjiesun-ustc/LGS-KT, encouraging further innovation in this field.

TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma.

To develop and validate a predictive model for predicting six-month outcome by integrating pretreatment MRI features and one-month treatment response after TACE. A total of 108 patients with 160hCCs from a single-arm, multicenter clinical trial (NCT03113955) were analyzed and served as the training cohort. An external multicenter dataset (ChiCTR2100046020) consisting of 63 patients with 99hCCs served as the test dataset. Radiomics model was constructed based on the selected features from pretreatment MR images. Univariate and multivariate logistic regression analysis of clinical and radiological factors were used to identify the independent predictors for the 6-month treatment response. A combined model was further constructed by incorporating one-month treatment response, selected clinical and radiological factors and radiomics signature. Among all the clinical and radiological features, only corona enhancement and one-month treatment response were selected. The combined model, named TRACE model (Treatment response at 1 month, RAdiomics and Corona Enhancement), with AUCs of 0.91 (training cohort) and 0.84 (test cohort). The TRACE model demonstrated a significantly higher AUC than the radiomics model (P = 0.001). High-risk and low-risk groups stratified by using the TRACE model also exhibited significant differences in overall survival (OS) (P < 0.001). In contrast, none of the published scoring systems, including ART, SNACOR or ABCR score, demonstrated significant differences between the risk groups in OS prediction. The TRACE model exhibited favorable predictive capability for six-month TACE response, and holds potential as a marker for long-term survival outcomes.

Low‐Carbon Optimal Scheduling of Integrated Energy System Based on the Master–Slave Game

ABSTRACTWith the integration of distributed power generation into the grid, the economic incentive trading market mechanism becomes an effective method to promote carbon emission reduction in microgrids. In this paper, the carbon flow of the integrated energy system is calculated, the carbon emission model and the carbon flow tracing model of the integrated energy system are established, and the optimization model aiming at low‐carbon operation of the integrated energy system is constructed based on the master–slave game model. For the user side of the energy system, the dynamic carbon price and electricity price established by the model play a good role in peaking and valley filling for the load part, improving the operation stability of the power system, realizing the optimal scheduling of the power grid system under the background of electric‐carbon coupling trading, and encouraging each microgrid entity to participate in the electric‐carbon coupling trading actively. The calculation results show that electric‐carbon coupling trading facilitates the flexible operation of power grid systems and improves economic benefits. When combined with carbon emission flow in operation, it can promote the low‐carbon and clean power system, encourage distributed renewable resources to connect to the power grid, reduce the carbon content of the power system, enable users to actively participate in low‐carbon demand response, and promote the effective carbon emission reduction of a multi‐microgrid system.

A detail tracking strategy analysis of a compound parabolic collector with a heat–pipe receiver based on three–dimensional ray–tracing model

A detail tracking strategy analysis of a compound parabolic collector with a heat–pipe receiver based on three–dimensional ray–tracing model

Dysregulated alveolar epithelial cell progenitor function and identity in Hermansky-Pudlak syndrome.

Hermansky-Pudlak syndrome (HPS) is a genetic disorder of endosomal protein trafficking associated with pulmonary fibrosis in specific subtypes, including HPS-1 and HPS-2. Single mutant HPS1 and HPS2 mice display increased fibrotic sensitivity while double mutant HPS1/2 mice exhibit spontaneous fibrosis with aging, which has been attributed to HPS mutations in alveolar epithelial type II (AT2) cells. We utilized HPS mouse models and human lung tissue to investigate mechanisms of AT2 cell dysfunction driving fibrotic remodeling in HPS. Starting at 8 weeks of age, HPS mice exhibited progressive loss of AT2 cell numbers. HPS AT2 cell function was impaired ex vivo and in vivo. Incorporating AT2 cell lineage tracing in HPS mice, we observed aberrant differentiation with increased AT2-derived alveolar epithelial type I cells. Transcriptomic analysis of HPS AT2 cells revealed elevated expression of genes associated with aberrant differentiation and p53 activation. Lineage tracing and organoid modeling studies demonstrated that HPS AT2 cells were primed to persist in a Krt8+ reprogrammed transitional state, mediated by p53 activity. Intrinsic AT2 progenitor cell dysfunction and p53 pathway dysregulation are novel mechanisms of disease in HPS-related pulmonary fibrosis, with the potential for early targeted intervention before the onset of fibrotic lung disease.

Tuft cells transdifferentiate to neural-like progenitor cells in the progression of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDA) is partly initiated through the transdifferentiation of acinar cells to metaplasia, which progresses to neoplasia and cancer. Tuft cells (TCs) are chemosensory cells not found in the normal pancreas but arise in cancer precursor lesions and diminish during progression to carcinoma. These metaplastic TCs (mTCs) suppress tumor progression through communication with the tumor microenvironment, but their fate during progression is unknown. To determine the fate of mTCs during PDA progression, we created a dual recombinase lineage trace model, wherein a pancreas-specific FlpO was used to induce tumorigenesis, while a tuft-cell specific Pou2f3CreERT/+ driver was used to induce expression of a tdTomato reporter. We found that mTCs in carcinoma transdifferentiate into neural-like progenitor cells (NRPs), a cell type associated with poor survival in patients. Using conditional knockout and overexpression systems, we found that Myc activity in mTCs is necessary and sufficient to induce this tuft-to-neuroendocrine transition (TNT).

TRACE system code benchmark of Station Blackout of a NuScale-like reactor and comparison with NuScale simulator

System code modeling is the primary tool used for design and licensing and operator training of advanced reactor technologies. While system codes have been well validated for existing reactor technologies, caution should be taken with respect to system code modeling capabilities in advanced reactors which have significant phenomenological differences in their designs. Within the scope of advanced reactors, NuScale Power is the first vendor to have their reactor design approved by the U.S. Nuclear Regulatory Commission. NuScale has developed a plant simulator which models a 12-unit NuScale plant capable of running operational or accident transients of each of the units. In an effort to assess the capabilities of the TRACE system code and perform a code benchmark, a NuScale-like reactor model was developed based on the Design Certificate Application. A Station-Blackout (SBO) accident scenario was modelled and compared with data obtained from the NuScale simulator. The SBO transient was run for a total of 8000 s and characteristics of the primary and secondary side systems were compared. Despite some differences attributed to the realistic approach of the simulator, the study found that the primary and secondary system characteristics over the course of the SBO transient showed reasonable agreement for trends and values observed in the NuScale simulator data and the TRACE model.

A ray tracing model of a V-configuration transit time difference flow meter based on the ray deflection effect

A ray tracing model of a V-configuration transit time difference flow meter based on the ray deflection effect

Simulation of the charged particle deflection from the sweeping magnet array in the Lunar Environment heliospheric X-ray imager.

The Lunar Environment heliospheric X-ray Imager (LEXI) is an instrument built to image x-rays from solar wind charge exchange in Earth's magnetosheath. Monitoring the position of the magnetopause at the inner boundary of the magnetosheath allows us to understand how magnetic reconnection regulates how energy from the solar wind is deposited into Earth's magnetosphere. LEXI is part of an upcoming lunar lander mission set to land in Mare Crisium. To repel unwanted charged particles, the instrument carries a permanent magnet array composed of 48 neodymium magnets. The array was designed to maximize charged particle deflection while minimizing stray magnetic fields, which could impact other instruments or spacecraft operation. A Runge-Kutta-based fully kinetic particle tracing model was created to evaluate the effectiveness of LEXI's unique charged particle deflector array. Combined with the other particle suppression measures of the instrument, including physical structures and filters, the simulations show proton and electron transmission to the LEXI detector is expected to be sufficiently reduced to allow successful imaging. The flexible simulation model can be generalized to be used in examining the magnetic deflector array effectiveness of other instruments whose signals could be compromised by unwanted charged particle contamination.

Why might there be lexical-prelexical feedback in speech recognition?

In reply to Magnuson, Crinnion, Luthra, Gaston, and Grubb (2023), we challenge their conclusion that on-line activation feedback improves word recognition. This type of feedback is instantiated in the TRACE model (McClelland & Elman, 1986) as top-down spread of activation from lexical to phoneme nodes. We give two main reasons why Magnuson et al.'s demonstration that activation feedback speeds up word recognition in TRACE is not informative about whether activation feedback helps humans recognize words. First, the same speed-up could be achieved by changing other parameters in TRACE. Second, more fundamentally, there is room for improvement in TRACE's performance only because the model, unlike Bayesian models, is suboptimal. We also challenge Magnuson et al.'s claim that the available empirical data support activation feedback. The data they base this claim on are open to alternative explanations and there are data against activation feedback that they do not discuss. We argue, therefore, that there are no computational or empirical grounds to conclude that activation feedback benefits human spoken-word recognition and indeed no theoretical grounds why activation feedback would exist. Other types of feedback, for example feedback to support perceptual learning, likely do exist, precisely because they can help listeners recognize words.

Optimizing bifacial PV performance: The impact of reflectors and free space luminescent solar concentrators on winter yield

In this study, we present a novel solar energy harvesting system incorporating free-space luminescent solar concentrators (FSLSCs) integrated with bifacial photovoltaic (PV) modules. The FSLSC design features a luminophore-doped waveguide, an angle- and wavelength-selective notch filter, and a Lambertian reflector, enabling efficient photon recycling. Unlike traditional luminescent solar concentrators, the FSLSC aims to emit photons into free space within a defined emission cone, enhancing light redirection towards PV modules. We developed a three-dimensional ray tracing model to analyze system performance, including different reflector configurations and emission cones. The study focuses on optimizing energy yield in urban settings, particularly during winter months, by examining the effects of diffuse and specular reflectors, and various FSLSC configurations. Our results demonstrate that FSLSCs can enhance winter energy production in the Netherlands by up to 60 %, compared to a conventional optimal tilt monofacial system. The findings highlight the potential of FSLSCs and specialized reflectors to increase PV system efficiency and offer flexible solutions for improving energy yield throughout the year, particularly during periods of high demand.

Water resource vulnerability assessment in Hubei Province: a case study

In view of the different views of academia on the weight allocation of vulnerability assessment indicators, this study creatively proposed a data-based objective evaluation framework of water resource vulnerability, and applied it to the evaluation of water resource vulnerability in Hubei Province. According to the conceptual model of DPSIR proposed by the United Nations, five vulnerability factors are proposed: driving force, pressure, state, influence and response. In this study, 15 indicators were selected and the projection tracing model was used to identify vulnerability. Aiming at the complex problem of optimization calculation of projection index function in the projection tracing model, the accelerated genetic algorithm is used to speed up the optimization speed, solves the optimization problem in the process of projection tracing, and determines the objective weight of all indicators. Example calculation shows that the model can deal with complex multi-index optimization problems, and is an effective way to solve the comprehensive evaluation of complex vulnerability, and the weighting method is important for the evaluation of water resources vulnerability. The results of this paper show that the combination of projection tracing method and machine learning algorithm can improve the efficiency, objectivity and accuracy of high-dimensional data analysis, and can provide scientific basis for policy makers.

The lung is a megakaryocyte outpost that can defend against thrombocytopenic attack.

Lung megakaryocytes (Mks) are a unique subset of Mks that are distinct from their bone marrow counterparts. Recent evidence suggests that lung Mks favor an immune phenotype, but have unclear contributions to the total platelet mass. In this issue of the JCI, Livada et al. used an array of complementary in vivo labeling and tracing models in mice to investigate a longstanding question of where lung Mks are derived. By combining these models with stressed conditions, the authors assessed the contribution of lung Mks to total platelet counts in a homeostatic and thrombocytopenic state. Mks were minor contributors to the circulating pool of platelets during homeostasis but increased output during thrombocytopenia. These findings add critical understanding to the development of lung Mks and demonstrate the dynamic potential of these specialized cells to respond to thrombocytopenia.